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old_adult_names.txt
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old_adult_names.txt
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1. Title of Database: adult
2. Sources:
(a) Original owners of database (name/phone/snail address/email address)
US Census Bureau.
(b) Donor of database (name/phone/snail address/email address)
Ronny Kohavi and Barry Becker,
Data Mining and Visualization
Silicon Graphics.
e-mail: [email protected]
(c) Date received (databases may change over time without name change!)
05/19/96
3. Past Usage:
(a) Complete reference of article where it was described/used
@inproceedings{kohavi-nbtree,
author={Ron Kohavi},
title={Scaling Up the Accuracy of Naive-Bayes Classifiers: a
Decision-Tree Hybrid},
booktitle={Proceedings of the Second International Conference on
Knowledge Discovery and Data Mining},
year = 1996,
pages={to appear}}
(b) Indication of what attribute(s) were being predicted
Salary greater or less than 50,000.
(b) Indication of study's results (i.e. Is it a good domain to use?)
Hard domain with a nice number of records.
The following results obtained using MLC++ with default settings
for the algorithms mentioned below.
Algorithm Error
-- ---------------- -----
1 C4.5 15.54
2 C4.5-auto 14.46
3 C4.5 rules 14.94
4 Voted ID3 (0.6) 15.64
5 Voted ID3 (0.8) 16.47
6 T2 16.84
7 1R 19.54
8 NBTree 14.10
9 CN2 16.00
10 HOODG 14.82
11 FSS Naive Bayes 14.05
12 IDTM (Decision table) 14.46
13 Naive-Bayes 16.12
14 Nearest-neighbor (1) 21.42
15 Nearest-neighbor (3) 20.35
16 OC1 15.04
17 Pebls Crashed. Unknown why (bounds WERE increased)
4. Relevant Information Paragraph:
Extraction was done by Barry Becker from the 1994 Census database. A set
of reasonably clean records was extracted using the following conditions:
((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
5. Number of Instances
48842 instances, mix of continuous and discrete (train=32561, test=16281)
45222 if instances with unknown values are removed (train=30162, test=15060)
Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
6. Number of Attributes
6 continuous, 8 nominal attributes.
7. Attribute Information:
age: continuous.
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
fnlwgt: continuous.
education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
education-num: continuous.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
sex: Female, Male.
capital-gain: continuous.
capital-loss: continuous.
hours-per-week: continuous.
native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
class: >50K, <=50K
8. Missing Attribute Values:
7% have missing values.
9. Class Distribution:
Probability for the label '>50K' : 23.93% / 24.78% (without unknowns)
Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)